A leading company in the rail industry is seeking a Lead Data Scientist to join its Data Science and Analytics team. This is the first seat on a newly formed data science team, with real autonomy over the methodology, the models, and how the analysis reaches its audience. Reporting to the Head of Data Science, the Lead Data Scientist is responsible for building the company's view of where risk sits in its customers' track, moving from descriptive analysis of defect and surface-condition trends to predictive risk modeling, while hiring and managing two additional team members. Salary +

Additional Benefits:

$165,000-$175,000 Medical, Dental, Vision Insurance 401K – company match

Location:

Shelton, CT Type of Position:

Direct Hire Responsibilities:

Trend the company's multi-modal test data over time – internal flaw detection, induction, and eddy current – for defect growth and surface condition degradation across its non-stop inspection programs in North America. Connect raw test measurements and their metadata to the physical conditions they represent: internal defects, surface conditions, rail flaws. Analyze defect and error types and frequency by subdivision to identify where risk is concentrated and how it moves. Apply risk-based models to estimate the probability and consequence of failure. Own the KPIs for the monthly operational review and customer account review meetings. Present findings to the commercial team in a form they can use in account conversations. Work with the analysis organization so that what the data shows about defect and surface-condition patterns reaches the analysts reviewing that track. Present risk findings to customers alongside the commercial team. Identify gaps in current data collection and recommend what the company should capture to support better analysis. Validate findings against field conditions with track engineering and testing teams. Hire and line manage the two further seats in the pod, and direct their work. Write and maintain documentation so that the analysis is transferable rather than held tacitly.

Requirements:

Statistical depth: probability, hypothesis testing, regression analysis, time series analysis SQL and Python, or equivalent analysis tooling Risk modelling or reliability engineering GIS or geospatial data analysis Experience applying statistical and risk-modelling methods to physical or engineered systems – rail, industrial, energy, or a comparable setting Demonstrable experience managing staff across the full employee lifecycle A credible and confident communicator, written and verbal, at all levels of a business Ability to make effective decisions and to keep calm under pressure High level of honesty and integrity A collaborative, team-first mindset

Preferred:

Bachelor's degree in applied mathematics, statistics, physics, engineering, or a related quantitative field; advanced degree welcome Typically 7+ years analyzing large technical datasets Linear referencing or corridor-level network data – for example pipeline integrity, highway, or utility corridor work Rail or transportation specifically Sensing technologies: ultrasonics, induction, eddy current Due to the high volume of applications we typically receive, we regret that we are not able to personally respond to all applications. However, if you are invited to take the next step in the process, you will typically be contacted within one week of submitting your application. #LI-DNI